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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Image Generation from Layout 32

Image Generation from Layout

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Bo Meng, Lili Yin, Weidong Sigal, Leonid Univ British Columbia Vector Inst Vancouver BC Canada

ISBN: (纸本)9781728132938

Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specified layout. To address these challenges, we propose a novel approach for layout-based image generation;we call it Layout2lm. Given the coarse spatial layout (bounding boxes + object categories), our model can generate a set of realistic images which have the correct objects in the desired locations. The representation of each object is disentangled into a specified/certain part (category) and an unspecified/uncertain part (appearance). The category is encoded using a word embedding and the appearance is distilled into a low-dimensional vector sampled from a normal distribution. Individual object representations are composed together using convolutional LSTM, to obtain an encoding of the complete layout, and then decoded to an image. Several loss terms are introduced to encourage accurate and diverse image generation. The proposed Layout2lm model significantly outperforms the previous state-of-the-art, boosting the best reported inception score by 24.66% and 28.57% on the very challenging COCO-Stuff and Visual Genome datasets, respectively. Extensive experiments also demonstrate our model's ability to generate complex and diverse images with many objects.

关键词： vision + Graphics vision Applications and Systems

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Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation

Anti-aliasing Semantic Reconstruction for Few-Shot Semantic ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Binghao Ding, Yao Jiao, Jianbin Ji, Xiangyang Ye, Qixiang Univ Chinese Acad Sci EECE PriSDL Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781665445092

Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples. However, this feature sharing mechanism inevitably causes semantic aliasing between novel classes when they have similar compositions of semantic concepts. In this paper, we reformulate few-shot segmentation as a semantic reconstruction problem, and convert base class features into a series of basis vectors which span a class-level semantic space for novel class reconstruction. By introducing contrastive loss, we maximize the orthogonality of basis vectors while minimizing semantic aliasing between classes. Within the reconstructed representation space, we further suppress interference from other classes by projecting query features to the support vector for precise semantic activation. Our proposed approach, referred to as anti-aliasing semantic reconstruction (ASR), provides a systematic yet interpretable solution for few-shot learning problems. Extensive experiments on PASCAL VOC and MS COCO datasets show that ASR achieves strong results compared with the prior works. Code will be released at ***/Bibkiller/ASR.

关键词： Training Support vector machines computer vision Systematics Codes Semantics Training data

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Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection

Informative and Consistent Correspondence Mining for Cross-D...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hou, Luwei Zhang, Yu Fu, Kui Li, Jia Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China SenseTime Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e., with object bounding boxes) to train object detectors for target domains that are weakly labeled (i.e., with image-level tags). Instead of domain-level distribution matching, as popularly adopted in the literature, we propose to learn pixel-wise cross-domain correspondences for more precise knowledge transfer. It is realized through a novel cross-domain co-attention scheme trained as region competition. In this scheme, the cross-domain correspondence module seeks for informative features on the target domain image, which if warped to the source domain image, could best explain its annotations. Meanwhile, a collaborative mask generator competes to mask out the relevant target image region to make the remaining features uninformative. Such competitive learning strives to correlate the full foreground in cross-domain image pairs, revealing the accurate object extent in target domain. To alleviate the ambiguity of inter-domain correspondence learning, a domain-cycle consistency regularizer is further proposed to leverage the more reliable intra-domain correspondence. The proposed approach achieves consistent improvements over existing approaches by a considerable margin, demonstrated by the experiments on various datasets.

关键词： computer vision Annotations Collaboration Object detection Detectors Generators pattern recognition

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LC-FDNet: Learned Lossless Image Compression with Frequency Decomposition Network

LC-FDNet: Learned Lossless Image Compression with Frequency ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Rhee, Hochang Jang, Yeong Il Kim, Seyun Cho, Nam Ik Seoul Natl Univ Dept ECE INMC Seoul South Korea Gauss Labs Inc Palo Alto CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent learning-based lossless image compression methods encode an image in the unit of subimages and achieve comparable performances to conventional non-learning algorithms. However, these methods do not consider the performance drop in the high frequency region, giving equal consideration to the low and high frequency areas. In this paper, we propose a new lossless image compression method that proceeds the encoding in a coarse-to-fine manner to separate and process low and high frequency regions differently. We initially compress the low frequency components and then use them as additional input for encoding the remaining high frequency region. The low frequency components act as a strong prior in this case, which leads to improved estimation in the high frequency area. In addition, we design the frequency decomposition process to be adaptive to color channel, spatial location, and image characteristics. As a result, our method derives an image-specific optimal ratio of low/high-frequency components. Experiments show that the proposed method achieves state-of-the-art performance for benchmark high-resolution datasets.

关键词： computer vision Image coding Codes Image color analysis Estimation Benchmark testing pattern recognition

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KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control

KeypointDeformer: Unsupervised 3D Keypoint Discovery for Sha...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jakab, Tomas Tucker, Richard Makadia, Ameesh Wu, Jiajun Snavely, Noah Kanazawa, Angjoo Univ Oxford Oxford England Univ Calif Berkeley Berkeley CA USA Stanford Univ Stanford CA 94305 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We introduce KeypointDeformer, a novel unsupervised method for shape control through automatically discovered 3D keypoints. We cast this as the problem of aligning a source 3D object to a target 3D object from the same object category. Our method analyzes the difference between the shapes of the two objects by comparing their latent representations. This latent representation is in the form of 3D keypoints that are learned in an unsupervised way. The difference between the 3D keypoints of the source and the target objects then informs the shape deformation algorithm that deforms the source object into the target object. The whole model is learned end-to-end and simultaneously discovers 3D keypoints while learning to use them for deforming object shapes. Our approach produces intuitive and semantically consistent control of shape deformations. Moreover, our discovered 3D keypoints are consistent across object category instances despite large shape variations. As our method is unsupervised, it can be readily deployed to new object categories without requiring annotations for 3D keypoints and deformations. Project page: http: //***/KeypointDeformer.

关键词： Deformable models Solid modeling Three-dimensional displays Shape control Shape Semantics pattern recognition

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RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs

RegNeRF: Regularizing Neural Radiance Fields for View Synthe...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Niemeyer, Michael Barron, Jonathan T. Mildenhall, Ben Sajjadi, Mehdi S. M. Geiger, Andreas Radwan, Noha Max Planck Inst Intelligent Syst Tubingen Germany Univ Tubingen Tubingen Germany Google Res Tubingen Germany Google Tubingen Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural Radiance Fields (NeRF) have emerged as a powerful representation for the task of novel view synthesis due to their simplicity and state-of-the-art performance. Though NeRF can produce photorealistic renderings of unseen viewpoints when many input views are available, its performance drops significantly when this number is reduced. We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training. We address this by regularizing the geometry and appearance of patches rendered from unobserved viewpoints, and annealing the ray sampling space during training. We additionally use a normalizing flow model to regularize the color of unobserved viewpoints. Our model outperforms not only other methods that optimize over a single scene, but in many cases also conditional models that are extensively pre-trained on large multi-view datasets.

关键词： Geometry Training Solid modeling Annealing Three-dimensional displays Computational modeling Rendering (computer graphics)

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A Neural Rendering Framework for Free-Viewpoint Relighting

A Neural Rendering Framework for Free-Viewpoint Relighting

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Zhang Chen, Anpei Zhang, Guli Wang, Chengyuan Ji, Yu Kutulakos, Kiriakos N. Yu, Jingyi ShanghaiTech Univ Shanghai Peoples R China Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai Univ Shanghai Peoples R China DGene Inc Baton Rouge LA USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9781728171685

We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis and relighting using multi-view image inputs. Existing neural rendering (NR) does not explicitly model the physical rendering process and hence has limited capabilities on relighting. RNR instead models image formation in terms of environment lighting, object intrinsic attributes, and light transport function (LTF), each corresponding to a learnable component. In particular, the incorporation of a physically based rendering process not only enables relighting but also improves the quality of view synthesis. Comprehensive experiments on synthetic and real data show that RNR provides a practical and effective solution for conducting free-viewpoint relighting.

关键词： Rendering (computer graphics)

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HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images

HODOR: High-level Object Descriptors for Object Re-segmentat...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Athar, Ali Luiten, Jonathon Hermans, Alexander Ramanan, Deva Leibe, Bastian Rhein Westfal TH Aachen Aachen Germany Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing state-of-the-art methods for Video Object Segmentation (VOS) learn low-level pixel-to-pixel correspondences between frames to propagate object masks across video. This requires a large amount of densely annotated video data, which is costly to annotate, and largely redundant since frames within a video are highly correlated. In light of this, we propose HODOR: a novel method that tackles VOS by effectively leveraging annotated static images for understanding object appearance and scene context. We encode object instances and scene information from an image frame into robust high-level descriptors which can then be used to re-segment those objects in different frames. As a result, HODOR achieves state-of-the-art performance on the DAVIS and YouTube-VOS benchmarks compared to existing methods trained without video annotations. With out any architectural modification, HODOR can also learn from video context around single annotated video frames by utilizing cyclic consistency, whereas other methods rely on dense, temporally consistent annotations. Source code: https://***/Ali2500/HODOR.

关键词： Training computer vision Image coding Codes Annotations Image annotation Object segmentation

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Spatial Commonsense Graph for Object Localisation in Partial Scenes

Spatial Commonsense Graph for Object Localisation in Partial...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Giuliari, Francesco Skenderi, Geri Cristani, Marco Wang, Yiming Del Bue, Alessio Ist Italiano Tecnol IIT Genoa Italy Univ Genoa Genoa Italy Univ Verona Verona Italy Fdn Bruno Kessler FBK Povo Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We solve object localisation in partial scenes, a new problem of estimating the unknown position of an object (e.g. where is the bag?) given a partial 3D scan of a scene. The proposed solution is based on a novel scene graph model, the Spatial Commonsense Graph (SCG), where objects are the nodes and edges define pairwise distances between them, enriched by concept nodes and relationships from a commonsense knowledge base. This allows SCG to better generalise its spatial inference to unknown 3D scenes. The SCG is used to estimate the unknown position of the target object in two steps: first, we feed the SCG into a novel Proximity Prediction Network, a graph neural network that uses attention to perform distance prediction between the node representing the target object and the nodes representing the observed objects in the SCG;second, we propose a Localisation Module based on circular intersection to estimate the object position using all the predicted pairwise distances in order to be independent of any reference system. We create a new dataset of partially reconstructed scenes to benchmark our method and baselines for object localisation in partial scenes, where our proposed method achieves the best localisation performance.

关键词： computer vision Three-dimensional displays Codes Urban areas Benchmark testing Graph neural networks pattern recognition

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Unsupervised Multi-Source Domain Adaptation for Person Re-Identification

Unsupervised Multi-Source Domain Adaptation for Person Re-Id...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bai, Zechen Wang, Zhigang Wang, Jian Hu, Di Ding, Errui Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9781665445092

Unsupervised domain adaptation (UDA) methods for person re-identification (re-ID) aim at transferring re-ID knowledge from labeled source data to unlabeled target data. Although achieving great success, most of them only use limited data from a single-source domain for model pre-training, making the rich labeled data insufficiently exploited. To make full use of the valuable labeled data, we introduce the multi-source concept into UDA person re-ID field, where multiple source datasets are used during training. However, because of domain gaps, simply combining different datasets only brings limited improvement. In this paper, we try to address this problem from two perspectives, i.e. domain-specific view and domain-fusion view. Two constructive modules are proposed, and they are compatible with each other. First, a rectification domain-specific batch normalization (RDSBN) module is explored to simultaneously reduce domain-specific characteristics and increase the distinctiveness of person features. Second, a graph convolutional network (GCN) based multi-domain information fusion (MDIF) module is developed, which minimizes domain distances by fusing features of different domains. The proposed method outperforms state-of-the-art UDA person re-ID methods by a large margin, and even achieves comparable performance to the supervised approaches without any post-processing techniques.

关键词： Training computer vision Adaptation models Fuses Data models pattern recognition Task analysis

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